Almost every industry in the world has understood the reality of machine learning across the industry. Do you want to be a machine learning engineer? In fact, the demand is high because of its super-fast analysis and hence data adds more value to it.
If you have “machine learning” in your CV, even basic knowledge then you are lucky and welcome to enter it. With many highly-paid jobs surrounding the market & the demand for ML is increasing day by day. Define your proper domain with experience or certification If you are a skilled person, most importantly have good communication skills.
From start to end, you will get a full roadmap to becoming a data science / ML engineer. I am going to explain a step-by-step procedure, keep on reading. This blog is going to be very important for you as it is an action-to-plan technique for you. If you are going to read this entire topic then you might be able to train your first model.
There are too many options in programming in data science or web development, and it is increasing day by day. The main reason might be a large amount of data increasing and new algorithms inventions,s and many other factors.
Step 1 : Learn a programming Language
Dozens of programming languages have different syntaxes & purposes. You will have to learn basic programming fundamentals in c/c++ and object-oriented programming language. If you are perfect in one programming language then jumping to some other is very easy. Now if you really want to become an ML engineer then python or R language is solidity perfect. What makes it perfect, you can move to any framework, for example, Django provides a framework for web development.
How To Learn Python Programming Language?
So, two modules will be your source of learning, which means these two platforms are freely available.
Scikit Learn :
All models of ML are implemented in it. You only have to put the dataset here for proper prediction. Pure working of its only the background of machine learning.
Here you need to model a neural network model and for this purpose, you should know about python language. A neural network is also machine learning and it has no need for feature engineering.
Step 2 : Learn Linear Algebra
Linear algera itself a very big topic. One side, if you only want to implement the models only then basic is enough for it. If you wanna become a professional, it will be helpful for you to understand the models belonigng to different domains. You will surely have learnt about basic algebra, stop, it is enough for clearly understanding model? Most buzzy words are vectors ,matrices, equations, and their basic operations.
Step 3 : Learn Statistics
Learning from Algebra to statistics gauranteed you to give up thinking why are you learnign these concepts. Here we go, you need a technique ‘parallel conquering technique’ for developing a sense of how it is working? If you completly learn step 1, it is also gaurantee that you are able to apply the ML algorithms. Make notes of basic topics of this course and try to learn how you can implement it in the model.
Step 4 : Learn Core ML Algorithms
In this step, you may know why to do, if not, then read whole passage. There are limited algorithms of machine learning that you can try to implement different dataset. The only one purpose is to know only how these algorithms are working and some deep concepts. For example, gradiant descent, linear regression model, logistic regression model, supervised learning & unsupervised learning and reinforcement learning. Reinforcement learning is a very big topic & a lot f research is remaining.
Step 5 : Learn Python Libraries
Libraries are resouce to some specific section e.g. graph. For example, if you really want to work on mathematics concept then working with libraries of mathematics. I would recommend two libraries Numpy & pandas. Keep a good practice on these two libraries would lead you to learn & understand sk learn code & decode. In this way, you would be able to make your ML journy beautiful and full of skills.
Step 6 : Learn Deployment
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